Overview

Brought to you by YData

Dataset statistics

Number of variables21
Number of observations2121
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 MiB
Average record size in memory969.9 B

Variable types

Numeric6
Text6
DateTime2
Categorical7

Alerts

Country has constant value "United States"Constant
Category has constant value "Furniture"Constant
Discount is highly overall correlated with ProfitHigh correlation
Postal Code is highly overall correlated with Region and 1 other fieldsHigh correlation
Profit is highly overall correlated with DiscountHigh correlation
Region is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
State is highly overall correlated with Postal Code and 1 other fieldsHigh correlation
Row ID has unique valuesUnique
Discount has 836 (39.4%) zerosZeros
Profit has 33 (1.6%) zerosZeros

Reproduction

Analysis started2025-10-31 21:09:04.182373
Analysis finished2025-10-31 21:09:05.942432
Duration1.76 second
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

Row ID
Real number (ℝ)

Unique 

Distinct2121
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5041.6436
Minimum1
Maximum9991
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-10-31T17:09:05.976264image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile495
Q12568
median5145
Q37534
95-th percentile9494
Maximum9991
Range9990
Interquartile range (IQR)4966

Descriptive statistics

Standard deviation2885.7403
Coefficient of variation (CV)0.57238086
Kurtosis-1.2027046
Mean5041.6436
Median Absolute Deviation (MAD)2480
Skewness-0.030720169
Sum10693326
Variance8327496.8
MonotonicityStrictly increasing
2025-10-31T17:09:06.016688image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11
 
< 0.1%
67141
 
< 0.1%
67831
 
< 0.1%
67801
 
< 0.1%
67791
 
< 0.1%
67681
 
< 0.1%
67661
 
< 0.1%
67621
 
< 0.1%
67571
 
< 0.1%
67561
 
< 0.1%
Other values (2111)2111
99.5%
ValueCountFrequency (%)
11
< 0.1%
21
< 0.1%
41
< 0.1%
61
< 0.1%
111
< 0.1%
241
< 0.1%
251
< 0.1%
281
< 0.1%
301
< 0.1%
371
< 0.1%
ValueCountFrequency (%)
99911
< 0.1%
99901
< 0.1%
99811
< 0.1%
99651
< 0.1%
99631
< 0.1%
99561
< 0.1%
99481
< 0.1%
99391
< 0.1%
99381
< 0.1%
99321
< 0.1%
Distinct1764
Distinct (%)83.2%
Missing0
Missing (%)0.0%
Memory size130.6 KiB
2025-10-31T17:09:06.107986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters29694
Distinct characters15
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1469 ?
Unique (%)69.3%

Sample

1st rowCA-2016-152156
2nd rowCA-2016-152156
3rd rowUS-2015-108966
4th rowCA-2014-115812
5th rowCA-2014-115812
ValueCountFrequency (%)
us-2015-1290074
 
0.2%
ca-2016-1577494
 
0.2%
ca-2014-1453874
 
0.2%
ca-2015-1001464
 
0.2%
ca-2017-1254514
 
0.2%
ca-2017-1001114
 
0.2%
us-2015-1381214
 
0.2%
ca-2015-1043464
 
0.2%
us-2017-1625584
 
0.2%
ca-2017-1444913
 
0.1%
Other values (1754)2082
98.2%
2025-10-31T17:09:06.226941image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15421
18.3%
-4242
14.3%
23244
10.9%
03243
10.9%
C1734
 
5.8%
A1734
 
5.8%
61652
 
5.6%
41606
 
5.4%
51588
 
5.3%
71583
 
5.3%
Other values (5)3647
12.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)29694
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
15421
18.3%
-4242
14.3%
23244
10.9%
03243
10.9%
C1734
 
5.8%
A1734
 
5.8%
61652
 
5.6%
41606
 
5.4%
51588
 
5.3%
71583
 
5.3%
Other values (5)3647
12.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)29694
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
15421
18.3%
-4242
14.3%
23244
10.9%
03243
10.9%
C1734
 
5.8%
A1734
 
5.8%
61652
 
5.6%
41606
 
5.4%
51588
 
5.3%
71583
 
5.3%
Other values (5)3647
12.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)29694
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
15421
18.3%
-4242
14.3%
23244
10.9%
03243
10.9%
C1734
 
5.8%
A1734
 
5.8%
61652
 
5.6%
41606
 
5.4%
51588
 
5.3%
71583
 
5.3%
Other values (5)3647
12.3%
Distinct889
Distinct (%)41.9%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Minimum2014-01-06 00:00:00
Maximum2017-12-30 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-31T17:09:06.266905image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:06.311399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct960
Distinct (%)45.3%
Missing0
Missing (%)0.0%
Memory size16.7 KiB
Minimum2014-01-10 00:00:00
Maximum2018-01-05 00:00:00
Invalid dates0
Invalid dates (%)0.0%
2025-10-31T17:09:06.356104image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:06.402053image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Ship Mode
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size128.1 KiB
Standard Class
1248 
Second Class
427 
First Class
327 
Same Day
 
119

Length

Max length14
Median length14
Mean length12.798208
Min length8

Characters and Unicode

Total characters27145
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSecond Class
2nd rowSecond Class
3rd rowStandard Class
4th rowStandard Class
5th rowStandard Class

Common Values

ValueCountFrequency (%)
Standard Class1248
58.8%
Second Class427
 
20.1%
First Class327
 
15.4%
Same Day119
 
5.6%

Length

2025-10-31T17:09:06.440430image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:06.470333image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
class2002
47.2%
standard1248
29.4%
second427
 
10.1%
first327
 
7.7%
same119
 
2.8%
day119
 
2.8%

Most occurring characters

ValueCountFrequency (%)
a4736
17.4%
s4331
16.0%
d2923
10.8%
2121
7.8%
l2002
7.4%
C2002
7.4%
S1794
 
6.6%
n1675
 
6.2%
r1575
 
5.8%
t1575
 
5.8%
Other values (8)2411
8.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a4736
17.4%
s4331
16.0%
d2923
10.8%
2121
7.8%
l2002
7.4%
C2002
7.4%
S1794
 
6.6%
n1675
 
6.2%
r1575
 
5.8%
t1575
 
5.8%
Other values (8)2411
8.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a4736
17.4%
s4331
16.0%
d2923
10.8%
2121
7.8%
l2002
7.4%
C2002
7.4%
S1794
 
6.6%
n1675
 
6.2%
r1575
 
5.8%
t1575
 
5.8%
Other values (8)2411
8.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27145
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a4736
17.4%
s4331
16.0%
d2923
10.8%
2121
7.8%
l2002
7.4%
C2002
7.4%
S1794
 
6.6%
n1675
 
6.2%
r1575
 
5.8%
t1575
 
5.8%
Other values (8)2411
8.9%
Distinct707
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size118.2 KiB
2025-10-31T17:09:06.589848image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters16968
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)7.5%

Sample

1st rowCG-12520
2nd rowCG-12520
3rd rowSO-20335
4th rowBH-11710
5th rowBH-11710
ValueCountFrequency (%)
sv-2036515
 
0.7%
cj-120109
 
0.4%
kl-165559
 
0.4%
je-157459
 
0.4%
lc-168859
 
0.4%
gb-145308
 
0.4%
la-167808
 
0.4%
jd-161508
 
0.4%
sc-200958
 
0.4%
jl-158358
 
0.4%
Other values (697)2030
95.7%
2025-10-31T17:09:06.736200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12566
15.1%
-2121
12.5%
01800
 
10.6%
51656
 
9.8%
2981
 
5.8%
7646
 
3.8%
8614
 
3.6%
6612
 
3.6%
3585
 
3.4%
9581
 
3.4%
Other values (30)4806
28.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)16968
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
12566
15.1%
-2121
12.5%
01800
 
10.6%
51656
 
9.8%
2981
 
5.8%
7646
 
3.8%
8614
 
3.6%
6612
 
3.6%
3585
 
3.4%
9581
 
3.4%
Other values (30)4806
28.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)16968
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
12566
15.1%
-2121
12.5%
01800
 
10.6%
51656
 
9.8%
2981
 
5.8%
7646
 
3.8%
8614
 
3.6%
6612
 
3.6%
3585
 
3.4%
9581
 
3.4%
Other values (30)4806
28.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)16968
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
12566
15.1%
-2121
12.5%
01800
 
10.6%
51656
 
9.8%
2981
 
5.8%
7646
 
3.8%
8614
 
3.6%
6612
 
3.6%
3585
 
3.4%
9581
 
3.4%
Other values (30)4806
28.3%
Distinct707
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size129.0 KiB
2025-10-31T17:09:06.843796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length22
Median length18
Mean length12.970297
Min length7

Characters and Unicode

Total characters27510
Distinct characters56
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique160 ?
Unique (%)7.5%

Sample

1st rowClaire Gute
2nd rowClaire Gute
3rd rowSean O'Donnell
4th rowBrosina Hoffman
5th rowBrosina Hoffman
ValueCountFrequency (%)
michael26
 
0.6%
john23
 
0.5%
lena22
 
0.5%
paul22
 
0.5%
bill22
 
0.5%
gary21
 
0.5%
patrick21
 
0.5%
rick20
 
0.5%
brian20
 
0.5%
liz20
 
0.5%
Other values (844)4046
94.9%
2025-10-31T17:09:06.985085image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a2589
 
9.4%
e2565
 
9.3%
2142
 
7.8%
n2139
 
7.8%
r2052
 
7.5%
i1637
 
6.0%
l1421
 
5.2%
o1278
 
4.6%
t1155
 
4.2%
s915
 
3.3%
Other values (46)9617
35.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)27510
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2589
 
9.4%
e2565
 
9.3%
2142
 
7.8%
n2139
 
7.8%
r2052
 
7.5%
i1637
 
6.0%
l1421
 
5.2%
o1278
 
4.6%
t1155
 
4.2%
s915
 
3.3%
Other values (46)9617
35.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27510
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2589
 
9.4%
e2565
 
9.3%
2142
 
7.8%
n2139
 
7.8%
r2052
 
7.5%
i1637
 
6.0%
l1421
 
5.2%
o1278
 
4.6%
t1155
 
4.2%
s915
 
3.3%
Other values (46)9617
35.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27510
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2589
 
9.4%
e2565
 
9.3%
2142
 
7.8%
n2139
 
7.8%
r2052
 
7.5%
i1637
 
6.0%
l1421
 
5.2%
o1278
 
4.6%
t1155
 
4.2%
s915
 
3.3%
Other values (46)9617
35.0%

Segment
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size119.9 KiB
Consumer
1113 
Corporate
646 
Home Office
362 

Length

Max length11
Median length8
Mean length8.8165959
Min length8

Characters and Unicode

Total characters18700
Distinct characters17
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowConsumer
2nd rowConsumer
3rd rowConsumer
4th rowConsumer
5th rowConsumer

Common Values

ValueCountFrequency (%)
Consumer1113
52.5%
Corporate646
30.5%
Home Office362
 
17.1%

Length

2025-10-31T17:09:07.088464image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:07.128994image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
consumer1113
44.8%
corporate646
26.0%
home362
 
14.6%
office362
 
14.6%

Most occurring characters

ValueCountFrequency (%)
o2767
14.8%
e2483
13.3%
r2405
12.9%
C1759
9.4%
m1475
7.9%
n1113
 
6.0%
s1113
 
6.0%
u1113
 
6.0%
f724
 
3.9%
t646
 
3.5%
Other values (7)3102
16.6%

Most occurring categories

ValueCountFrequency (%)
(unknown)18700
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o2767
14.8%
e2483
13.3%
r2405
12.9%
C1759
9.4%
m1475
7.9%
n1113
 
6.0%
s1113
 
6.0%
u1113
 
6.0%
f724
 
3.9%
t646
 
3.5%
Other values (7)3102
16.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18700
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o2767
14.8%
e2483
13.3%
r2405
12.9%
C1759
9.4%
m1475
7.9%
n1113
 
6.0%
s1113
 
6.0%
u1113
 
6.0%
f724
 
3.9%
t646
 
3.5%
Other values (7)3102
16.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18700
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o2767
14.8%
e2483
13.3%
r2405
12.9%
C1759
9.4%
m1475
7.9%
n1113
 
6.0%
s1113
 
6.0%
u1113
 
6.0%
f724
 
3.9%
t646
 
3.5%
Other values (7)3102
16.6%

Country
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size128.5 KiB
United States
2121 

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters27573
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowUnited States
2nd rowUnited States
3rd rowUnited States
4th rowUnited States
5th rowUnited States

Common Values

ValueCountFrequency (%)
United States2121
100.0%

Length

2025-10-31T17:09:07.187029image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:07.204065image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
united2121
50.0%
states2121
50.0%

Most occurring characters

ValueCountFrequency (%)
t6363
23.1%
e4242
15.4%
U2121
 
7.7%
n2121
 
7.7%
i2121
 
7.7%
d2121
 
7.7%
2121
 
7.7%
S2121
 
7.7%
a2121
 
7.7%
s2121
 
7.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)27573
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t6363
23.1%
e4242
15.4%
U2121
 
7.7%
n2121
 
7.7%
i2121
 
7.7%
d2121
 
7.7%
2121
 
7.7%
S2121
 
7.7%
a2121
 
7.7%
s2121
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)27573
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t6363
23.1%
e4242
15.4%
U2121
 
7.7%
n2121
 
7.7%
i2121
 
7.7%
d2121
 
7.7%
2121
 
7.7%
S2121
 
7.7%
a2121
 
7.7%
s2121
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)27573
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t6363
23.1%
e4242
15.4%
U2121
 
7.7%
n2121
 
7.7%
i2121
 
7.7%
d2121
 
7.7%
2121
 
7.7%
S2121
 
7.7%
a2121
 
7.7%
s2121
 
7.7%

City
Text

Distinct371
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size121.0 KiB
2025-10-31T17:09:07.311004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length16
Median length13
Mean length9.3328619
Min length4

Characters and Unicode

Total characters19795
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique156 ?
Unique (%)7.4%

Sample

1st rowHenderson
2nd rowHenderson
3rd rowFort Lauderdale
4th rowLos Angeles
5th rowLos Angeles
ValueCountFrequency (%)
city206
 
6.8%
new196
 
6.5%
york193
 
6.4%
san177
 
5.9%
los154
 
5.1%
angeles154
 
5.1%
philadelphia111
 
3.7%
francisco102
 
3.4%
seattle97
 
3.2%
houston81
 
2.7%
Other values (400)1551
51.3%
2025-10-31T17:09:07.465975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
e1865
 
9.4%
a1640
 
8.3%
o1625
 
8.2%
i1316
 
6.6%
n1300
 
6.6%
l1231
 
6.2%
s1001
 
5.1%
r988
 
5.0%
t946
 
4.8%
901
 
4.6%
Other values (40)6982
35.3%

Most occurring categories

ValueCountFrequency (%)
(unknown)19795
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e1865
 
9.4%
a1640
 
8.3%
o1625
 
8.2%
i1316
 
6.6%
n1300
 
6.6%
l1231
 
6.2%
s1001
 
5.1%
r988
 
5.0%
t946
 
4.8%
901
 
4.6%
Other values (40)6982
35.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19795
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e1865
 
9.4%
a1640
 
8.3%
o1625
 
8.2%
i1316
 
6.6%
n1300
 
6.6%
l1231
 
6.2%
s1001
 
5.1%
r988
 
5.0%
t946
 
4.8%
901
 
4.6%
Other values (40)6982
35.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19795
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e1865
 
9.4%
a1640
 
8.3%
o1625
 
8.2%
i1316
 
6.6%
n1300
 
6.6%
l1231
 
6.2%
s1001
 
5.1%
r988
 
5.0%
t946
 
4.8%
901
 
4.6%
Other values (40)6982
35.3%

State
Categorical

High correlation 

Distinct48
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size119.3 KiB
California
444 
New York
236 
Texas
202 
Pennsylvania
125 
Illinois
123 
Other values (43)
991 

Length

Max length20
Median length14
Mean length8.5318246
Min length4

Characters and Unicode

Total characters18096
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)0.2%

Sample

1st rowKentucky
2nd rowKentucky
3rd rowFlorida
4th rowCalifornia
5th rowCalifornia

Common Values

ValueCountFrequency (%)
California444
20.9%
New York236
 
11.1%
Texas202
 
9.5%
Pennsylvania125
 
5.9%
Illinois123
 
5.8%
Washington114
 
5.4%
Ohio93
 
4.4%
Florida85
 
4.0%
Virginia52
 
2.5%
Colorado51
 
2.4%
Other values (38)596
28.1%

Length

2025-10-31T17:09:07.508356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
california444
18.0%
new272
 
11.0%
york236
 
9.6%
texas202
 
8.2%
pennsylvania125
 
5.1%
illinois123
 
5.0%
washington114
 
4.6%
ohio93
 
3.8%
florida85
 
3.4%
virginia53
 
2.1%
Other values (43)719
29.2%

Most occurring characters

ValueCountFrequency (%)
a2269
12.5%
i2153
11.9%
n1745
 
9.6%
o1585
 
8.8%
r1168
 
6.5%
l1090
 
6.0%
e1052
 
5.8%
s1010
 
5.6%
C559
 
3.1%
f447
 
2.5%
Other values (36)5018
27.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)18096
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a2269
12.5%
i2153
11.9%
n1745
 
9.6%
o1585
 
8.8%
r1168
 
6.5%
l1090
 
6.0%
e1052
 
5.8%
s1010
 
5.6%
C559
 
3.1%
f447
 
2.5%
Other values (36)5018
27.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18096
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a2269
12.5%
i2153
11.9%
n1745
 
9.6%
o1585
 
8.8%
r1168
 
6.5%
l1090
 
6.0%
e1052
 
5.8%
s1010
 
5.6%
C559
 
3.1%
f447
 
2.5%
Other values (36)5018
27.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18096
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a2269
12.5%
i2153
11.9%
n1745
 
9.6%
o1585
 
8.8%
r1168
 
6.5%
l1090
 
6.0%
e1052
 
5.8%
s1010
 
5.6%
C559
 
3.1%
f447
 
2.5%
Other values (36)5018
27.7%

Postal Code
Real number (ℝ)

High correlation 

Distinct454
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean55726.556
Minimum1040
Maximum99301
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-10-31T17:09:07.547309image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1040
5-th percentile10009
Q122801
median60505
Q390032
95-th percentile98103
Maximum99301
Range98261
Interquartile range (IQR)67231

Descriptive statistics

Standard deviation32261.888
Coefficient of variation (CV)0.57893203
Kurtosis-1.4944761
Mean55726.556
Median Absolute Deviation (MAD)29544
Skewness-0.16116852
Sum1.1819603 × 108
Variance1.0408294 × 109
MonotonicityNot monotonic
2025-10-31T17:09:07.587265image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1003559
 
2.8%
1002447
 
2.2%
1001144
 
2.1%
1000942
 
2.0%
9412241
 
1.9%
1914039
 
1.8%
9411037
 
1.7%
9810337
 
1.7%
9004534
 
1.6%
9811531
 
1.5%
Other values (444)1710
80.6%
ValueCountFrequency (%)
10401
 
< 0.1%
17522
 
0.1%
18101
 
< 0.1%
18417
0.3%
18523
 
0.1%
20388
0.4%
21381
 
< 0.1%
21481
 
< 0.1%
21496
0.3%
21511
 
< 0.1%
ValueCountFrequency (%)
993011
 
< 0.1%
992072
 
0.1%
986612
 
0.1%
986321
 
< 0.1%
985022
 
0.1%
982261
 
< 0.1%
981981
 
< 0.1%
9811531
1.5%
9810529
1.4%
9810337
1.7%

Region
Categorical

High correlation 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size111.6 KiB
West
707 
East
601 
Central
481 
South
332 

Length

Max length7
Median length4
Mean length4.8368694
Min length4

Characters and Unicode

Total characters10259
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSouth
2nd rowSouth
3rd rowSouth
4th rowWest
5th rowWest

Common Values

ValueCountFrequency (%)
West707
33.3%
East601
28.3%
Central481
22.7%
South332
15.7%

Length

2025-10-31T17:09:07.625898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:07.649898image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
west707
33.3%
east601
28.3%
central481
22.7%
south332
15.7%

Most occurring characters

ValueCountFrequency (%)
t2121
20.7%
s1308
12.7%
e1188
11.6%
a1082
10.5%
W707
 
6.9%
E601
 
5.9%
C481
 
4.7%
n481
 
4.7%
r481
 
4.7%
l481
 
4.7%
Other values (4)1328
12.9%

Most occurring categories

ValueCountFrequency (%)
(unknown)10259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
t2121
20.7%
s1308
12.7%
e1188
11.6%
a1082
10.5%
W707
 
6.9%
E601
 
5.9%
C481
 
4.7%
n481
 
4.7%
r481
 
4.7%
l481
 
4.7%
Other values (4)1328
12.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown)10259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
t2121
20.7%
s1308
12.7%
e1188
11.6%
a1082
10.5%
W707
 
6.9%
E601
 
5.9%
C481
 
4.7%
n481
 
4.7%
r481
 
4.7%
l481
 
4.7%
Other values (4)1328
12.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown)10259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
t2121
20.7%
s1308
12.7%
e1188
11.6%
a1082
10.5%
W707
 
6.9%
E601
 
5.9%
C481
 
4.7%
n481
 
4.7%
r481
 
4.7%
l481
 
4.7%
Other values (4)1328
12.9%
Distinct375
Distinct (%)17.7%
Missing0
Missing (%)0.0%
Memory size132.7 KiB
2025-10-31T17:09:07.728940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters31815
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st rowFUR-BO-10001798
2nd rowFUR-CH-10000454
3rd rowFUR-TA-10000577
4th rowFUR-FU-10001487
5th rowFUR-TA-10001539
ValueCountFrequency (%)
fur-fu-1000427016
 
0.8%
fur-ch-1000114615
 
0.7%
fur-ch-1000264715
 
0.7%
fur-ch-1000288014
 
0.7%
fur-fu-1000147314
 
0.7%
fur-ch-1000377414
 
0.7%
fur-ta-1000109513
 
0.6%
fur-fu-1000486413
 
0.6%
fur-ch-1000428713
 
0.6%
fur-fu-1000484812
 
0.6%
Other values (365)1982
93.4%
2025-10-31T17:09:07.914009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
07346
23.1%
-4242
13.3%
F3078
9.7%
13078
9.7%
U3078
9.7%
R2121
 
6.7%
41061
 
3.3%
31009
 
3.2%
2932
 
2.9%
7784
 
2.5%
Other values (10)5086
16.0%

Most occurring categories

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
07346
23.1%
-4242
13.3%
F3078
9.7%
13078
9.7%
U3078
9.7%
R2121
 
6.7%
41061
 
3.3%
31009
 
3.2%
2932
 
2.9%
7784
 
2.5%
Other values (10)5086
16.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
07346
23.1%
-4242
13.3%
F3078
9.7%
13078
9.7%
U3078
9.7%
R2121
 
6.7%
41061
 
3.3%
31009
 
3.2%
2932
 
2.9%
7784
 
2.5%
Other values (10)5086
16.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown)31815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
07346
23.1%
-4242
13.3%
F3078
9.7%
13078
9.7%
U3078
9.7%
R2121
 
6.7%
41061
 
3.3%
31009
 
3.2%
2932
 
2.9%
7784
 
2.5%
Other values (10)5086
16.0%

Category
Categorical

Constant 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size120.3 KiB
Furniture
2121 

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters19089
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFurniture
2nd rowFurniture
3rd rowFurniture
4th rowFurniture
5th rowFurniture

Common Values

ValueCountFrequency (%)
Furniture2121
100.0%

Length

2025-10-31T17:09:07.948508image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:07.969159image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
furniture2121
100.0%

Most occurring characters

ValueCountFrequency (%)
u4242
22.2%
r4242
22.2%
F2121
11.1%
n2121
11.1%
i2121
11.1%
t2121
11.1%
e2121
11.1%

Most occurring categories

ValueCountFrequency (%)
(unknown)19089
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
u4242
22.2%
r4242
22.2%
F2121
11.1%
n2121
11.1%
i2121
11.1%
t2121
11.1%
e2121
11.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown)19089
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
u4242
22.2%
r4242
22.2%
F2121
11.1%
n2121
11.1%
i2121
11.1%
t2121
11.1%
e2121
11.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown)19089
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
u4242
22.2%
r4242
22.2%
F2121
11.1%
n2121
11.1%
i2121
11.1%
t2121
11.1%
e2121
11.1%

Sub-Category
Categorical

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size119.4 KiB
Furnishings
957 
Chairs
617 
Tables
319 
Bookcases
228 

Length

Max length11
Median length9
Mean length8.5785007
Min length6

Characters and Unicode

Total characters18195
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowBookcases
2nd rowChairs
3rd rowTables
4th rowFurnishings
5th rowTables

Common Values

ValueCountFrequency (%)
Furnishings957
45.1%
Chairs617
29.1%
Tables319
 
15.0%
Bookcases228
 
10.7%

Length

2025-10-31T17:09:07.999408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-31T17:09:08.049561image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
furnishings957
45.1%
chairs617
29.1%
tables319
 
15.0%
bookcases228
 
10.7%

Most occurring characters

ValueCountFrequency (%)
s3306
18.2%
i2531
13.9%
n1914
10.5%
r1574
8.7%
h1574
8.7%
a1164
 
6.4%
u957
 
5.3%
F957
 
5.3%
g957
 
5.3%
C617
 
3.4%
Other values (8)2644
14.5%

Most occurring categories

ValueCountFrequency (%)
(unknown)18195
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s3306
18.2%
i2531
13.9%
n1914
10.5%
r1574
8.7%
h1574
8.7%
a1164
 
6.4%
u957
 
5.3%
F957
 
5.3%
g957
 
5.3%
C617
 
3.4%
Other values (8)2644
14.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown)18195
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s3306
18.2%
i2531
13.9%
n1914
10.5%
r1574
8.7%
h1574
8.7%
a1164
 
6.4%
u957
 
5.3%
F957
 
5.3%
g957
 
5.3%
C617
 
3.4%
Other values (8)2644
14.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown)18195
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s3306
18.2%
i2531
13.9%
n1914
10.5%
r1574
8.7%
h1574
8.7%
a1164
 
6.4%
u957
 
5.3%
F957
 
5.3%
g957
 
5.3%
C617
 
3.4%
Other values (8)2644
14.5%
Distinct380
Distinct (%)17.9%
Missing0
Missing (%)0.0%
Memory size191.7 KiB
2025-10-31T17:09:08.243961image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Length

Max length90
Median length70
Mean length43.035361
Min length15

Characters and Unicode

Total characters91278
Distinct characters74
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.3%

Sample

1st rowBush Somerset Collection Bookcase
2nd rowHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back
3rd rowBretford CR4500 Series Slim Rectangular Table
4th rowEldon Expressions Wood and Plastic Desk Accessories, Cherry Wood
5th rowChromcraft Rectangular Conference Tables
ValueCountFrequency (%)
chair479
 
3.6%
global284
 
2.1%
table219
 
1.6%
hon190
 
1.4%
eldon181
 
1.4%
wall180
 
1.4%
clock179
 
1.3%
chairs174
 
1.3%
x169
 
1.3%
series167
 
1.3%
Other values (661)11053
83.3%
2025-10-31T17:09:08.403003image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
11156
 
12.2%
e7627
 
8.4%
a6416
 
7.0%
o5392
 
5.9%
r4871
 
5.3%
l4741
 
5.2%
i4522
 
5.0%
s3983
 
4.4%
t3609
 
4.0%
n3552
 
3.9%
Other values (64)35409
38.8%

Most occurring categories

ValueCountFrequency (%)
(unknown)91278
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
11156
 
12.2%
e7627
 
8.4%
a6416
 
7.0%
o5392
 
5.9%
r4871
 
5.3%
l4741
 
5.2%
i4522
 
5.0%
s3983
 
4.4%
t3609
 
4.0%
n3552
 
3.9%
Other values (64)35409
38.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown)91278
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
11156
 
12.2%
e7627
 
8.4%
a6416
 
7.0%
o5392
 
5.9%
r4871
 
5.3%
l4741
 
5.2%
i4522
 
5.0%
s3983
 
4.4%
t3609
 
4.0%
n3552
 
3.9%
Other values (64)35409
38.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown)91278
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
11156
 
12.2%
e7627
 
8.4%
a6416
 
7.0%
o5392
 
5.9%
r4871
 
5.3%
l4741
 
5.2%
i4522
 
5.0%
s3983
 
4.4%
t3609
 
4.0%
n3552
 
3.9%
Other values (64)35409
38.8%

Sales
Real number (ℝ)

Distinct1636
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean349.83489
Minimum1.892
Maximum4416.174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-10-31T17:09:08.442659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.892
5-th percentile10.02
Q147.04
median182.22
Q3435.168
95-th percentile1317.492
Maximum4416.174
Range4414.282
Interquartile range (IQR)388.128

Descriptive statistics

Standard deviation503.17914
Coefficient of variation (CV)1.4383332
Kurtosis15.91681
Mean349.83489
Median Absolute Deviation (MAD)151.884
Skewness3.3491681
Sum741999.8
Variance253189.25
MonotonicityNot monotonic
2025-10-31T17:09:08.481675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.166
 
0.3%
141.966
 
0.3%
14.766
 
0.3%
301.966
 
0.3%
18.846
 
0.3%
603.925
 
0.2%
41.965
 
0.2%
31.9844
 
0.2%
242.3524
 
0.2%
77.9524
 
0.2%
Other values (1626)2069
97.5%
ValueCountFrequency (%)
1.8921
< 0.1%
1.9881
< 0.1%
2.0321
< 0.1%
2.3282
0.1%
2.7841
< 0.1%
2.911
< 0.1%
2.962
0.1%
3.3121
< 0.1%
3.481
< 0.1%
3.9842
0.1%
ValueCountFrequency (%)
4416.1741
< 0.1%
4404.91
< 0.1%
4297.6441
< 0.1%
4228.7041
< 0.1%
4007.841
< 0.1%
3785.2921
< 0.1%
3610.8481
< 0.1%
3504.91
< 0.1%
3406.6641
< 0.1%
3393.681
< 0.1%

Quantity
Real number (ℝ)

Distinct14
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.7850071
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-10-31T17:09:08.512186image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile8
Maximum14
Range13
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2516203
Coefficient of variation (CV)0.59487876
Kurtosis2.2214521
Mean3.7850071
Median Absolute Deviation (MAD)1
Skewness1.3452701
Sum8028
Variance5.069794
MonotonicityNot monotonic
2025-10-31T17:09:08.540627image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
3534
25.2%
2503
23.7%
5253
11.9%
4247
11.6%
1193
 
9.1%
6121
 
5.7%
7115
 
5.4%
958
 
2.7%
858
 
2.7%
1012
 
0.6%
Other values (4)27
 
1.3%
ValueCountFrequency (%)
1193
 
9.1%
2503
23.7%
3534
25.2%
4247
11.6%
5253
11.9%
6121
 
5.7%
7115
 
5.4%
858
 
2.7%
958
 
2.7%
1012
 
0.6%
ValueCountFrequency (%)
148
 
0.4%
135
 
0.2%
126
 
0.3%
118
 
0.4%
1012
 
0.6%
958
 
2.7%
858
 
2.7%
7115
5.4%
6121
5.7%
5253
11.9%

Discount
Real number (ℝ)

High correlation  Zeros 

Distinct11
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.17392268
Minimum0
Maximum0.7
Zeros836
Zeros (%)39.4%
Negative0
Negative (%)0.0%
Memory size16.7 KiB
2025-10-31T17:09:08.568467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.2
Q30.3
95-th percentile0.6
Maximum0.7
Range0.7
Interquartile range (IQR)0.3

Descriptive statistics

Standard deviation0.18154728
Coefficient of variation (CV)1.0438391
Kurtosis0.21947188
Mean0.17392268
Median Absolute Deviation (MAD)0.2
Skewness0.94204855
Sum368.89
Variance0.032959417
MonotonicityNot monotonic
2025-10-31T17:09:08.593835image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
0836
39.4%
0.2615
29.0%
0.3222
 
10.5%
0.6138
 
6.5%
0.176
 
3.6%
0.475
 
3.5%
0.554
 
2.5%
0.1552
 
2.5%
0.3227
 
1.3%
0.715
 
0.7%
ValueCountFrequency (%)
0836
39.4%
0.176
 
3.6%
0.1552
 
2.5%
0.2615
29.0%
0.3222
 
10.5%
0.3227
 
1.3%
0.475
 
3.5%
0.4511
 
0.5%
0.554
 
2.5%
0.6138
 
6.5%
ValueCountFrequency (%)
0.715
 
0.7%
0.6138
 
6.5%
0.554
 
2.5%
0.4511
 
0.5%
0.475
 
3.5%
0.3227
 
1.3%
0.3222
 
10.5%
0.2615
29.0%
0.1552
 
2.5%
0.176
 
3.6%

Profit
Real number (ℝ)

High correlation  Zeros 

Distinct1777
Distinct (%)83.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6993271
Minimum-1862.3124
Maximum1013.127
Zeros33
Zeros (%)1.6%
Negative714
Negative (%)33.7%
Memory size16.7 KiB
2025-10-31T17:09:08.630221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1862.3124
5-th percentile-153.3456
Q1-12.849
median7.7748
Q333.7266
95-th percentile190.4298
Maximum1013.127
Range2875.4394
Interquartile range (IQR)46.5756

Descriptive statistics

Standard deviation136.04925
Coefficient of variation (CV)15.639054
Kurtosis37.003383
Mean8.6993271
Median Absolute Deviation (MAD)22.3512
Skewness-2.2854556
Sum18451.273
Variance18509.397
MonotonicityNot monotonic
2025-10-31T17:09:08.666276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
033
 
1.6%
10.39365
 
0.2%
2.95685
 
0.2%
15.5254
 
0.2%
60.3924
 
0.2%
23.0284
 
0.2%
-10.17364
 
0.2%
4.28044
 
0.2%
45.2944
 
0.2%
9.88564
 
0.2%
Other values (1767)2050
96.7%
ValueCountFrequency (%)
-1862.31241
< 0.1%
-1665.05221
< 0.1%
-1002.78361
< 0.1%
-968.88331
< 0.1%
-814.48321
< 0.1%
-786.7441
< 0.1%
-734.52641
< 0.1%
-653.28341
< 0.1%
-630.8821
< 0.1%
-619.5961
< 0.1%
ValueCountFrequency (%)
1013.1271
< 0.1%
770.3521
< 0.1%
746.40781
< 0.1%
700.981
< 0.1%
673.88161
< 0.1%
629.011
< 0.1%
610.86241
< 0.1%
609.71571
< 0.1%
585.5521
< 0.1%
580.53941
< 0.1%

Interactions

2025-10-31T17:09:05.548903image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.502125image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.720932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.916675image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.155635image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.345549image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.580896image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.539432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.754629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.005192image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.187404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.380059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.616629image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.575151image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.785243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.035606image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.218830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.414099image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.648262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.605062image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.817218image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.064596image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.248516image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.445291image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.679735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.635766image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.849132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.094478image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.278552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.482979image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.712687image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.680213image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:04.885467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.126014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.312499image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-31T17:09:05.515785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-31T17:09:08.695139image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
DiscountPostal CodeProfitQuantityRegionRow IDSalesSegmentShip ModeStateSub-Category
Discount1.0000.038-0.723-0.0070.4860.0290.0930.0330.0270.4920.494
Postal Code0.0381.000-0.0260.0270.9210.0150.0060.0450.0000.9600.019
Profit-0.723-0.0261.0000.1390.057-0.0370.1880.0000.0000.1880.223
Quantity-0.0070.0270.1391.0000.0410.0080.3890.0000.0000.0000.000
Region0.4860.9210.0570.0411.0000.0460.0130.0000.0000.9900.015
Row ID0.0290.015-0.0370.0080.0461.000-0.0160.0000.0400.0990.000
Sales0.0930.0060.1880.3890.013-0.0161.0000.0420.0000.1030.261
Segment0.0330.0450.0000.0000.0000.0000.0421.0000.0000.1060.000
Ship Mode0.0270.0000.0000.0000.0000.0400.0000.0001.0000.0650.025
State0.4920.9600.1880.0000.9900.0990.1030.1060.0651.0000.000
Sub-Category0.4940.0190.2230.0000.0150.0000.2610.0000.0250.0001.000

Missing values

2025-10-31T17:09:05.774427image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-31T17:09:05.894178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
01CA-2016-15215611/8/201611/11/2016Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-BO-10001798FurnitureBookcasesBush Somerset Collection Bookcase261.960020.0041.9136
12CA-2016-15215611/8/201611/11/2016Second ClassCG-12520Claire GuteConsumerUnited StatesHendersonKentucky42420SouthFUR-CH-10000454FurnitureChairsHon Deluxe Fabric Upholstered Stacking Chairs, Rounded Back731.940030.00219.5820
24US-2015-10896610/11/201510/18/2015Standard ClassSO-20335Sean O'DonnellConsumerUnited StatesFort LauderdaleFlorida33311SouthFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table957.577550.45-383.0310
36CA-2014-1158126/9/20146/14/2014Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestFUR-FU-10001487FurnitureFurnishingsEldon Expressions Wood and Plastic Desk Accessories, Cherry Wood48.860070.0014.1694
411CA-2014-1158126/9/20146/14/2014Standard ClassBH-11710Brosina HoffmanConsumerUnited StatesLos AngelesCalifornia90032WestFUR-TA-10001539FurnitureTablesChromcraft Rectangular Conference Tables1706.184090.2085.3092
524US-2017-1569097/16/20177/18/2017Second ClassSF-20065Sandra FlanaganConsumerUnited StatesPhiladelphiaPennsylvania19140EastFUR-CH-10002774FurnitureChairsGlobal Deluxe Stacking Chair, Gray71.372020.30-1.0196
625CA-2015-1063209/25/20159/30/2015Standard ClassEB-13870Emily BurnsConsumerUnited StatesOremUtah84057WestFUR-TA-10000577FurnitureTablesBretford CR4500 Series Slim Rectangular Table1044.630030.00240.2649
728US-2015-1506309/17/20159/21/2015Standard ClassTB-21520Tracy BlumsteinConsumerUnited StatesPhiladelphiaPennsylvania19140EastFUR-BO-10004834FurnitureBookcasesRiverside Palais Royal Lawyers Bookcase, Royale Cherry Finish3083.430070.50-1665.0522
830US-2015-1506309/17/20159/21/2015Standard ClassTB-21520Tracy BlumsteinConsumerUnited StatesPhiladelphiaPennsylvania19140EastFUR-FU-10004848FurnitureFurnishingsHoward Miller 13-3/4" Diameter Brushed Chrome Round Wall Clock124.200030.2015.5250
937CA-2016-11759012/8/201612/10/2016First ClassGH-14485Gene HaleCorporateUnited StatesRichardsonTexas75080CentralFUR-FU-10003664FurnitureFurnishingsElectrix Architect's Clamp-On Swing Arm Lamp, Black190.920050.60-147.9630
Row IDOrder IDOrder DateShip DateShip ModeCustomer IDCustomer NameSegmentCountryCityStatePostal CodeRegionProduct IDCategorySub-CategoryProduct NameSalesQuantityDiscountProfit
21119932CA-2015-10494811/13/201511/17/2015Standard ClassKH-16510Keith HerreraConsumerUnited StatesSan BernardinoCalifornia92404WestFUR-BO-10004357FurnitureBookcasesO'Sullivan Living Dimensions 3-Shelf Bookcases683.332040.15-40.1960
21129938CA-2016-1648896/3/20166/6/2016Second ClassCP-12340Christine PhanCorporateUnited StatesLos AngelesCalifornia90049WestFUR-TA-10001676FurnitureTablesHon 61000 Series Interactive Training Tables71.088020.20-1.7772
21139939CA-2016-16982412/12/201612/17/2016Standard ClassNS-18640Noel StaavosCorporateUnited StatesNew York CityNew York10009EastFUR-FU-10004864FurnitureFurnishingsEldon 500 Class Desk Accessories60.350050.0019.9155
21149948CA-2017-1215596/1/20176/3/2017Second ClassHW-14935Helen WassermanCorporateUnited StatesIndianapolisIndiana46203CentralFUR-CH-10003746FurnitureChairsHon 4070 Series Pagoda Round Back Stacking Chairs1925.880060.00539.2464
21159956CA-2015-14159312/14/201512/16/2015Second ClassDB-12970Darren BuddCorporateUnited StatesLos AngelesCalifornia90045WestFUR-TA-10002622FurnitureTablesBush Andora Conference Table, Maple/Graphite Gray Finish273.568020.2010.2588
21169963CA-2015-1680883/19/20153/22/2015First ClassCM-12655Corinna MitchellHome OfficeUnited StatesHoustonTexas77041CentralFUR-BO-10004218FurnitureBookcasesBush Heritage Pine Collection 5-Shelf Bookcase, Albany Pine Finish, *Special Order383.465640.32-67.6704
21179965CA-2016-14637412/5/201612/10/2016Second ClassHE-14800Harold EngleCorporateUnited StatesNewarkDelaware19711EastFUR-FU-10002671FurnitureFurnishingsElectrix 20W Halogen Replacement Bulb for Zoom-In Desk Lamp13.400010.006.4320
21189981US-2015-1514359/6/20159/9/2015Second ClassSW-20455Shaun WeienConsumerUnited StatesLafayetteLouisiana70506SouthFUR-TA-10001039FurnitureTablesKI Adjustable-Height Table85.980010.0022.3548
21199990CA-2014-1104221/21/20141/23/2014Second ClassTB-21400Tom BoeckenhauerConsumerUnited StatesMiamiFlorida33180SouthFUR-FU-10001889FurnitureFurnishingsUltra Door Pull Handle25.248030.204.1028
21209991CA-2017-1212582/26/20173/3/2017Standard ClassDB-13060Dave BrooksConsumerUnited StatesCosta MesaCalifornia92627WestFUR-FU-10000747FurnitureFurnishingsTenex B1-RE Series Chair Mats for Low Pile Carpets91.960020.0015.6332